The Development of Breast Cancer Classification Methods: Healthcare Professionals Early Detection

The aim of this research is to improve the classification performance of the Random Forest algorithm in breast cancer diagnosis through the application of feature optimization techniques. By focusing on the Breast Cancer Coimbra dataset, the study conducts experiments to assess the classification accuracy before and after feature optimization. The findings demonstrate that the Random Forest algorithm achieves an accuracy of 75.91% without feature optimization. However, after implementing feature optimization using the genetic algorithm, the accuracy significantly improves to 81.29%. A comparative analysis with other feature optimization methods reveals the superiority of the genetic algorithm in enhancing classification accuracy. These results highlight the effectiveness of the proposed optimization approach in improving the Random Forest algorithm's performance for breast cancer classification. The research contributes to the advancement of breast cancer diagnosis methods, offering valuable insights for healthcare professionals in early disease detection. Furthermore, the application of data mining concepts aids in understanding the factors influencing breast cancer, providing a basis for future investigations into the utilization of genetic algorithms in classifying other diseases.